I found a link that explains it.
http://reifman-sem.blogspot.com/2007/04/some-of-students-wanted-review-of.html
It is
$$Degree \;of\;Freedom (DF) = Total \;Elements - Total \;Free \;Parameters$$ [correct me if I'm wrong]
Here is a model under my project:
A latent model; 3 Latent Variables, 3 Indicators for each of two Latent Variables and the last Latent Variable with 4 indicators.
Hence, Total of 3 Latent and 10 Measured Variables.
Nature of Variable Relationships
Two latent variables are correlated with each other, while the same last latent variable is negatively correlated both of the other two.
All indicator / measured variables are expected to positively estimate unto their respective latent variables
By my own calculations
Free Parameters :
3 = Covariance of all latent variables
3 = Variance of all latent variables
10 = Covariances between latent and Manifest / Indicator Variables
10 = Residual Indicator variance
3 = Construct Variance
7 = freely estimated factor loadings (one loading per factor being fixed at 1)
[ Out of 3 manifest variables, there are 2 factor loadings not fixed each for 2 latent variables
Out of 4 manifest variables there are 3 factor loadings not fixed for the last latent variable ]
Now for Total Elements
Using the advised formula (from the link above):
$$\frac{(I^2) + I}{2}$$
where $$I = Manifest \;Variables$$
$$\frac{(10^2) + 10}{2} = 55$$
Hence,
\begin{align} Degree \;of\;Freedom (DF) & = 55 - (3 + 3 + 10 + 10 + 3 + 3 + 7)\\ &= (55-36)\\ &= 19 \end{align}
Was my identifications of the elements and free parameters correct? Was my calculation correct?
I'll be needing this for further calculation to determine sample size for validation project.
